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Autopager is a Python package which detects and classifies pagination links.
License is MIT.
Install autopager with pip::
pip install autopager
Autopager depends on a few other packages like lxml and python-crfsuite; it will try install them automatically, but you may need to consult with installation docs for these packages if installation fails.
.. _lxml: http://lxml.de/ .. _python-crfsuite: http://python-crfsuite.readthedocs.org/en/latest/
Autopager works in Python 3.6+.
autopager.urls function returns a list of pagination URLs::
import autopager import requests autopager.urls(requests.get('http://my-url.org')) ['http://my-url.org/page/1', 'http://my-url.org/page/3', 'http://my-url.org/page/4']
autopager.select function returns all pagination <a> elements
as parsel.SelectorList (the same object as scrapy
response.css / response.xpath methods return).
autopager.extract function returns a list of (link_type, link) tuples
where link_type is one of "PAGE", "PREV", "NEXT" and link
is a parsel.Selector instance.
These functions accept HTML page contents (as an unicode string), requests Response or scrapy Response as a first argument.
By default, a prebuilt extraction model is used. If you want to use
your own model use autopager.AutoPager class; it has the same
methods but allows to provide model path or model itself::
import autopager pager = autopager.AutoPager('my_model.crf') pager.urls(html)
You also have to use AutoPager class if you've cloned repository from git; prebuilt model is only available in pypi releases.
Web pages can be very different; autopager tries to work for all websites,
but some errors are inevitable. As a very rough estimate, expect it to work
properly for 9/10 paginators on websites sampled from 1M international
most popular websites (according to Alexa Top_).
.. _Alexa Top: https://support.alexa.com/hc/en-us/articles/200449834-Does-Alexa-have-a-list-of-its-top-ranked-websites-
Autopager uses machine learning to detect paginators. It classifies
<a> HTML elements into 4 classes:
To do that it uses features like link text, css class names, URL parts and right/left contexts. CRF_ model is used for learning.
Web page is represented as a sequence of <a> elements. Only <a>
elements with non-empty href attributes are in this sequence.
See also: https://github.com/TeamHG-Memex/autopager/blob/master/notebooks/Training.ipynb
.. _CRF: https://en.wikipedia.org/wiki/Conditional_random_field
Data is stored at autopager/data. Raw HTML source code is in autopager/data/html folder. Annotations are in autopager/data/data.csv file; elements are stored as CSS selectors.
Training data is annotated with 5 non-empty classes:
Because LAST and FIRST are relatively rare they are converted to PAGE by pagination model. By using these classes during annotation it can be possible to make model predict them as well in future, with more training examples.
To add a new page to training data save it to an html file and add a row to the data.csv file. It is helpful to use http://selectorgadget.com/ extension to get CSS selectors.
Don't worry if your CSS selectors don't return <a> elements directly
(it is easy to occasionally select a parent or a child of an <a> element
when using SelectorGadget). If a selection itself is not <a> element
then parent <a> elements and children <a> elements are tried, this is
usually what is wanted because <a> tags are not nested on valid websites.
When using SelectorGadget pay special attention not to select anything other than pagination elements. Always check element count displayed by SelectorGadget and compare it to a number of elements you wanted to select.
Some websites change their DOM after rendering. This rarely affect paginator
elements, but sometimes it can happen. To prevent it instead of downloading
HTML file using "Save As.." browser menu option it is better to use
"Copy Outer HTML" in developer tools or render HTML using a headless browser
(e.g. Splash_). If you do so make sure to put UTF-8 encoding to data.csv,
regardless of page encoding defined in HTTP headers or <meta> tags.
.. _Splash: https://github.com/scrapinghub/splash
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